Spatiotemporal behaviour of a male wild boar, sus scrofa, in the pre-Alps of the canton Fribourg.

Projet for the course “Patterns and trends in environmental data”

Abstract

In September 2019, a male wild boar was captured in the pre-Alps of Fribourg. It was equipped with a GPS collar and its movements could be followed for almost 2 whole years. The tracking of this wild boar was analyzed according to 4 aspects: global analysis of its movements, analysis of important points of presence, analysis of the use of the land cover and finally analysis of its activity patterns. The results show a great variability both in the territory occupied and in the use of the different covers. Hunting shows an important influence on its general activity. The results give a direction to future analyses that will be carried out on other individuals in the pre-Alps in order to confirm the elements found during this project.

No studies have been conducted on the movements of wild boar in the Pre-Alps. These analyses are done in the framework of a pre-study. The data are the property of the state of Fribourg and must be treated confidentially.

Introduction and research aims

Wild boars, sus scrofa, are common mammals in Europe. The study of their movements and habits is used in particular within the framework of the management of their populations but also for the prevention of damage caused to agricultural crops. Many studies have been done to understand the spatio-temporal behavior of wild boars but none has been published on wild boars in the Pre-Alps. The pre-Alps present a particular territory due to their steep nature and their landscape coverage.

In this study, the movements of a male wild boar located in the Fribourg pre-Alps were analyzed over almost two years. The first goal of these analyses was to define the animal’s home range and its use of it, but also to see if landscape aspects (altitude, land cover) influence its use of space. The second goal was to analyze its habits and the effect of an anthropogenic disturbance (hunting in this case) on its habits.

This study was done in part from a global analysis to a more detailed analysis. Four elements guided the analysis with the following questions: - The analysis of its general home range: where is the boar located? What are the movements it has made and when? Are there differences between seasons in its occupation of space? - Analysis of areas of high density of presence: did the boar stay in certain places for a long time or did it go back and forth? What is present in these areas? - Analysis of surface cover: what is found in this boar’s home range? How did it use the surface cover? What is found in areas of high density presence? - Analysis of its habits: when is it most active? Are there differences in the use of ground cover? What is the impact of hunting on his habits?

Material and Methods

Data set

The data set used in this project comes from the Forest and Nature Department of the canton of Fribourg. In September 2019, a male wild boar was captured in the Fribourg pre-Alps and fitted with a GPS collar. This collar transmitted the position of the animal every hour from 6pm to 6am and then once at noon. The collar was programmed by the service based on the literature mentioning that the wild boar is a nocturnal animal. Location data could thus be obtained until July 2021. The animal then suddenly disappeared and so did the GPS transmission.

The data set contains 9431 lines, each corresponding to a location defined by X and Y coordinates according to the Swiss coordinate system (LV95/LV03, crs = 2056). The base file contains the following columns:

• Date: date of the survey
• Time: time of the survey
• Altitude: altitude of the GPS point
• X: X coordinate
• Y: Y coordinate

Preprocessing

This data set required some additional columns derived from the base file data to allow for further analysis to answer the questions outlined in the introduction to this project.A first visual analysis of the file in excel was done to observe the data.

The analysis of the “Altitude” column revealed values that could not correspond to the region where the boar was located. Indeed, the highest point in the region is at 2014m (Dent de Lys) and some location points exceeded this value. These points were removed. The same was done for the location points below 600m altitude.

Fig 1: Map of the region

The dates were analyzed and a subclassification by months and years was done. These data were added as new columns to the available data. Date and time were also merged together in a new column.A “Daytime” column has been added. This one is based on the “Timelag” column. If the time difference is less than 65 minutes, it concerns measurements taken at night (from 6pm to 6am), if this difference is greater than 360 minutes, it concerns measurements taken during the day (between 6am and 6pm). The column therefore contains the information Day or Night.

In order to calculate the activity pattern of this boar, I decided to base my calculations on its speed. The speed is calculated by the distance traveled in a certain time lapse. For that, a “Timelag” column was created which indicates the time elapsed between two locations, based on the “Datetime” column created during the previous manipulation of the “Date” and “Time” data. Then, a “steplength” column was created. This one indicates the distance between two rows, i.e. two location points. Finally, a “Speed” column has been created. This one is the result of the “Steplength” column divided by the “Timelag” column (Speed = Steplength/Timelag).

Finally, to analyze the influence of hunting on the activity of this boar, two new columns were created. The first one is the hunting pressure which was evaluated according to the number of species hunted at the same time on a given date. This information was provided by the Forest and Nature Department of the Canton of Fribourg. Based on this information, four categories were established for hunting pressure:

• 0 - no hunting

• 1 - only wild boar hunting

• 2 - hunting of wild boar + 1 other species

• 3 - wild boar hunting + 2 other species

An additional column was created to indicate whether or not hunting was taking place, based on the hunting pressure (0 = no hunting, 1/2/3 = hunting).

Some analyses could only be done with the year 2020, the only full year of data. This is why a second data_frame was created with only the points of 2020, subset of the original data frame.

Methods

I would like to point out that all of the methods described below can be applied to other similar data sets. These same manipulations will be used to study the movements of other wild boars in this same region in a future study.

Global analysis of the home range

Wild boars are territorial. In order to understand the extent of the male boar’s territory in this study, a visual analysis of its presence was performed. I performed a mcp derived from the location points of this boar to identify its home range. For this purpose, the function st_convex_hull has been applied. It takes a point cloud as input and draws a convex hull around the most distant vertices. The area was then calculated using the chull.area function and compared to the results obtained on ArcGis Pro for validation. The home range was analysed for all the data from 2019 to 2021

The altitude at which this boar was located is an important element considering the region in which it is located. Therefore, a boxplot analysis was performed to see the average elevation the boar was located at as a function of the months in 2020.

Density and path

To visualize the areas where wild boar were most often (high density of presence), an analysis via ArcGis Pro was performed by Kernel Density approach. Following this result, and in order to understand if these areas correspond to areas where the wild boar stayed for a long time or if it came back all the time, an analysis of the path by ggplot with the geom_path function was performed for each month in 2020.

Land cover and use of it

With the help of ArcGis Pro, a geopackage regrouping the data of the departements of agriculture and forests of the canton of Fribourg for the year 2020 has been created. This was then imported into R-studio. A column was added with the translation in English of the different types of surfaces for a better understanding. By analyzing the different types of surfaces and thinking about the behavior that a wild boar could have in them, I decided to create three surface overclasses: grassland areas which includes meadows, pastures and other rather open surfaces, forests and finally the surfaces corresponding to woodlands outside forests (Hedges-, fields- and riverbank woods). To facilitate the analysis of the data, I decided to work only with the main surface categories as well as the surface classes I created. For elements requiring detail such as a particular field, I worked with ArcGis Pro. The 2020 wild boar data frame and the geopackage data were then combined into a single data frame for further analysis.

A ggplot was made to visualize the presence of wild boar on the different terrains.

To find out the exact use of surface types by wild boar, the percentage of location points present in each surface type was calculated. This manipulation was then refined to identify the type of surface used according to the time of day and also according to the months.

Activity patterns

The speed of the boar was used as a basis for analyzing its activity rate. The GPS collar was programmed to transmit its position once an hour during the night from 6:00 p.m. to 6:00 a.m. and then once at noon, based on the boar’s nighttime activity. In order to verify this, two subsets were created: one for the day and one for the night. The average of the two subsets was calculated and then they were compared. For a better visualization, a boxplot was made. A day/night comparison was made by boxplot according to the months in 2020.

Finally, as speed can also depend on the type of surface on which the boar moves, a boxplot analysis of the different speeds according to the type of surface was performed.

Effect of Hunting

Thurfjell et al. demonstrated in 2013 that hunting had an influence on boar movement speed. To understand the influence of hunting on this male boar, a boxplot analysis of travel speeds by hunting pressure was performed.

To analyze whether hunting also induced a change in the boar’s surface type use patterns, the percentage of location points in a surface type as a function of whether hunting occurred was performed.

Results

Home range and vertical migration

This male boar was moving in the region of the Fribourg pre-Alps. Its home range was limited by the freeway to the west and by the Sarine river to the east. Its home range from 2019 to 2021 was 158.97 km2.

Fig 2: Home range of the wild boar from september 2019 to july 2021

The analysis of the altitude at which the boar were located in 2020 shows that a vertical migration to higher altitudes took place in spring until June. From July onwards, it can be observed that the boar again descended to lower altitudes. For the rest of the year 2020, the boar was located at approximately the same altitudes.

Fig 3: Altitude were the wild boar was depending on the month in 2020

Horizontal migration and land cover use in 2020

The point density analysis shows several areas of high density, including one in the northwest near the highway (Figure 4). The point analysis (Figure 5) of wild boar presence by month does not show that wild boar remained for a long time (minimum one month) in the same area. Path analysis by month confirms this trend, indicating that the boar was moving back and forth within its home range (Figure 6).

Fig 4: Density of presence of the wild boar in 2020

Fig 5: Location points by month in 2020

Fig 6: Path analysis by month in 2020

All the analyses carried out concerning the use of the different types of surface show that the wild boar was mainly located in the forest. Its presence in the forest did not vary much between months. Its presence in summer pastures was more important in May, June and July 2020 than in the other months. In January 2020, about 20% of the wild boar were located in areas not identified in the available geopackage compared to 10% in December 2020.

Fig 7: Land cover use per month in 2020

The wild boar were primarily located in the forest during the day (between 6:00 am and 6:00 pm) and diversified the areas where they were located at night.

Fig 8: Land cover use depending on daytime for each month in 2020

Activity patterns

The speed of the boar does not show a big visual difference from month to month. It averages 0.64 m/min during the day (6am to 6pm) and 5.04 m/min at night (6pm to 6am).

Fig 9: Speed depending on daytime

Fig 10: Speed depending on daytime for each month

The boar moved slower on average in the forest than in other surface types.

##                            Landcover    Speed
## 1                            Forests 3.499243
## 2 Hedges, fields and riverbank woods 5.761268
## 3                    Litter surfaces 2.623450
## 4                  Permanent meadows 6.735279
## 5                 Permanent pastures 6.049965
## 6                    Summer pastures 5.403049
## 7             Tree, overlaying areas 6.292032

Effect of hunting

Discussion

Discussion discussion discussion discussion

These results were validated by personal visualization. A detailed statistical analysis is necessary to be able to affirm if the trends observed on the obtained graphs are significant or not. This analysis will be carried out in a second time, outside the framework of this work.

R-Code

#Analyzing path in 2020 in a more funny way 

p <- ggplot(jojo2020, aes(X, Y)) + geom_path(show.legend = FALSE, mapping = aes(color = "indianred4")) + transition_time(Date) + labs(title = "Date : {frame_time}")
path2020 <- animate(p, duration = 120, render = gifski_renderer())
anim_save("jojo_path2020.gif", path2020)